Transducers with origin information

TL;DR

This paper introduces origin information into regular string-to-string transducers, enabling machine-independent characterization, polynomial-time learning, and effective subclass descriptions, enhancing understanding and learnability of these models.

Contribution

It extends the semantics of regular transducers to include origin information, providing new characterizations and learning algorithms for these enriched models.

Findings

Origin information integrated into transducer semantics

Polynomial-time Angluin-style learning algorithms developed

Characterizations of subclasses like one-way and first-order transducers achieved

Abstract

Call a string-to-string transducer regular if it can be realised by one of the following equivalent models: mso transductions, two-way deterministic automata with output, and streaming transducers with registers. This paper proposes to treat origin information as part of the semantics of a regular string-to-string transducer. With such semantics, the model admits a machine-independent characterisation, Angluin-style learning in polynomial time, as well as effective characterisations of natural subclasses such as one-way transducers or first-order definable transducers.